Update inference_manager.py

inference_manager.py

@@ -205,12 +205,12 @@ class InferenceManager:
             print(ckpt_dir)
             pipe = DiffusionPipeline.from_pretrained(
                 ckpt_dir,
-                #vae=vae,#!TEST! remove vae
+                vae=vae,
                 #unet=unet,
                 torch_dtype=torch.bfloat16,
                 use_safetensors=True,
                 #variant="fp16",
-                #custom_pipeline   = "lpw_stable_diffusion_xl",#!TEST! remove prompt embedding
+                custom_pipeline   = "lpw_stable_diffusion_xl",
             )
             #pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
             clip_skip = cfg.get("clip_skip", 1)
@@ -501,7 +501,7 @@ class ModelManager:
         if not images:
             raise Exception(f"face images not provided")
         start = time.time()
-        model.base_model_pipeline.to("cuda")
+        ip_model.pipe.to("cuda")
         if not self.app:
             self.app = FaceAnalysis(name="buffalo_l", providers=['CUDAExecutionProvider'])#, 'CPUExecutionProvider'
             self.app.prepare(ctx_id=0, det_size=(512, 512))
@@ -524,7 +524,7 @@ class ModelManager:
         prompt_str = cfg.get("prompt", "{prompt}").replace("{prompt}", p)
         #generator = torch.Generator(model.base_model_pipeline.device).manual_seed(seed)
         print(f"generate: p={p}, np={negative_prompt}, steps={steps}, guidance_scale={guidance_scale}, size={width},{height}, seed={seed}")
-        #print(f"device: embedding={average_embedding.device}, generator={generator.device}, ip_model={ip_model.device}, pipe={model.base_model_pipeline.device}")
+        print(f"device: embedding={average_embedding.device}, generator={generator.device}, ip_model={ip_model.pipe}, pipe={model.base_model_pipeline.device}")
         images = ip_model.generate(
             prompt=prompt_str,
             negative_prompt=negative_prompt,